Usbrth

I am trying to understand how to prove the following, which seems to be a quite useful insight in terms of linear optimization. Unfortunately, I have a hard time with projective geometry. I'd greatly appreciate if someone could explain me how this can be done.

Suppose we have an $n$-dimensional polytope $P subseteq mathbbR^n$
and two distinct vertices $u$ and $v$. I want to show that there
exists a projective transformation $P rightarrow P'$ such that the
transformed vertices $u'$ and $v'$ have the smallest, respectively the
largest, $x_n$ coordinate among all vertices of $P'$.

Thanks in advance!

UPDATE: I think that this statement is not true if we replace "projective transformation" with "affine transformation". It might be true if we restrict to neighboring vertices $u$ and $v$ but I am not quite sure about that either. So if we suppose that this doesn't work in affine geometry, but it does in projective geometry, then I'd reckon that we somehow have to exploit the ability to map facets to the infinity hyperplane..

I guess it is not very rigorous to say about projective transformations of subsets of an affine space, beacuse they should deal with points at infinity, which formally do not belong to the space. So I have the following hand-waving idea. Since the polytope $P$ is convex, there exist hyperplanes $H_uni u$ and $H_vni v$ supporting $P$. If they are parallel then we draw $x$-axis perpendicularly to them. Otherwise we move the intersection $H_ucap H_v$ to infinity by a projective transformation, which will make the hyperplanes $H_u$ and $H_v$ parallel.